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-rw-r--r--src/tutorial/clustering/SameSizeKMeansAlgorithm.java82
1 files changed, 41 insertions, 41 deletions
diff --git a/src/tutorial/clustering/SameSizeKMeansAlgorithm.java b/src/tutorial/clustering/SameSizeKMeansAlgorithm.java
index 574a02e9..91da44c5 100644
--- a/src/tutorial/clustering/SameSizeKMeansAlgorithm.java
+++ b/src/tutorial/clustering/SameSizeKMeansAlgorithm.java
@@ -57,8 +57,7 @@ import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.utilities.datastructures.arrays.IntegerArrayQuickSort;
import de.lmu.ifi.dbs.elki.utilities.datastructures.arrays.IntegerComparator;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterConstraint;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterEqualConstraint;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
@@ -112,7 +111,7 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
List<? extends NumberVector<?>> means = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction());
// Setup cluster assignment store
List<ModifiableDBIDs> clusters = new ArrayList<>();
- for (int i = 0; i < k; i++) {
+ for(int i = 0; i < k; i++) {
clusters.add(DBIDUtil.newHashSet(relation.size() / k + 2));
}
@@ -128,7 +127,7 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
// Wrap result
Clustering<MeanModel<V>> result = new Clustering<>("k-Means Samesize Clustering", "kmeans-samesize-clustering");
final NumberVector.Factory<V, ?> factory = RelationUtil.getNumberVectorFactory(relation);
- for (int i = 0; i < clusters.size(); i++) {
+ for(int i = 0; i < clusters.size(); i++) {
V mean = factory.newNumberVector(means.get(i).getColumnVector().getArrayRef());
result.addToplevelCluster(new Cluster<>(clusters.get(i), new MeanModel<>(mean)));
}
@@ -149,15 +148,16 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
// The actual storage
final WritableDataStore<Meta> metas = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, Meta.class);
// Build the metadata, track the two nearest cluster centers.
- for (DBIDIter id = relation.iterDBIDs(); id.valid(); id.advance()) {
+ for(DBIDIter id = relation.iterDBIDs(); id.valid(); id.advance()) {
Meta c = new Meta(k);
V fv = relation.get(id);
- for (int i = 0; i < k; i++) {
+ for(int i = 0; i < k; i++) {
c.dists[i] = df.doubleDistance(fv, means.get(i));
- if (i > 0) {
- if (c.dists[i] < c.dists[c.primary]) {
+ if(i > 0) {
+ if(c.dists[i] < c.dists[c.primary]) {
c.primary = i;
- } else if (c.dists[i] > c.dists[c.secondary]) {
+ }
+ else if(c.dists[i] > c.dists[c.secondary]) {
c.secondary = i;
}
}
@@ -184,9 +184,9 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
DBIDArrayIter id = tids.iter();
// Initialization phase:
- for (int start = 0; start < tids.size();) {
+ for(int start = 0; start < tids.size();) {
tids.sort(start, tids.size(), comp);
- for (id.seek(start); id.valid();) {
+ for(id.seek(start); id.valid();) {
Meta c = metas.get(id);
// Assigning to best cluster - which cannot be full yet!
ModifiableDBIDs cluster = clusters.get(c.primary);
@@ -194,18 +194,18 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
start++;
id.advance();
// Now the cluster may have become completely filled:
- if (cluster.size() == maxsize) {
+ if(cluster.size() == maxsize) {
final int full = c.primary;
// Refresh the not yet assigned objects where necessary:
- for (; id.valid(); id.advance()) {
+ for(; id.valid(); id.advance()) {
Meta ca = metas.get(id);
- if (ca.primary == full) {
+ if(ca.primary == full) {
// Update the best index:
- for (int i = 0; i < k; i++) {
- if (i == full || clusters.get(i).size() >= maxsize) {
+ for(int i = 0; i < k; i++) {
+ if(i == full || clusters.get(i).size() >= maxsize) {
continue;
}
- if (ca.primary == full || ca.dists[i] < ca.dists[ca.primary]) {
+ if(ca.primary == full || ca.dists[i] < ca.dists[ca.primary]) {
ca.primary = i;
}
}
@@ -232,15 +232,15 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
* @param df Distance function
*/
protected void updateDistances(Relation<V> relation, List<? extends NumberVector<?>> means, final WritableDataStore<Meta> metas, PrimitiveDoubleDistanceFunction<NumberVector<?>> df) {
- for (DBIDIter id = relation.iterDBIDs(); id.valid(); id.advance()) {
+ for(DBIDIter id = relation.iterDBIDs(); id.valid(); id.advance()) {
Meta c = metas.get(id);
V fv = relation.get(id);
// Update distances to means.
c.secondary = -1;
- for (int i = 0; i < k; i++) {
+ for(int i = 0; i < k; i++) {
c.dists[i] = df.doubleDistance(fv, means.get(i));
- if (c.primary != i) {
- if (c.secondary < 0 || c.dists[i] < c.dists[c.secondary]) {
+ if(c.primary != i) {
+ if(c.secondary < 0 || c.dists[i] < c.dists[c.secondary]) {
c.secondary = i;
}
}
@@ -277,7 +277,7 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
};
// List for sorting cluster preferences
int[] preferences = new int[k];
- for (int i = 0; i < k; i++) {
+ for(int i = 0; i < k; i++) {
preferences[i] = i;
}
// Comparator for this list.
@@ -285,28 +285,28 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
// Initialize transfer lists:
ArrayModifiableDBIDs[] transfers = new ArrayModifiableDBIDs[k];
- for (int i = 0; i < k; i++) {
+ for(int i = 0; i < k; i++) {
transfers[i] = DBIDUtil.newArray();
}
- for (int iter = 0; maxiter < 0 || iter < maxiter; iter++) {
+ for(int iter = 0; maxiter < 0 || iter < maxiter; iter++) {
updateDistances(relation, means, metas, df);
tids.sort(comp);
int active = 0; // Track if anything has changed
- for (DBIDIter id = tids.iter(); id.valid(); id.advance()) {
+ for(DBIDIter id = tids.iter(); id.valid(); id.advance()) {
Meta c = metas.get(id);
ModifiableDBIDs source = clusters.get(c.primary);
IntegerArrayQuickSort.sort(preferences, pcomp.select(c));
boolean transferred = false;
- for (int i : preferences) {
- if (i == c.primary) {
+ for(int i : preferences) {
+ if(i == c.primary) {
continue; // Cannot transfer to the same cluster!
}
ModifiableDBIDs dest = clusters.get(i);
// Can we pair this transfer?
- for (DBIDMIter other = transfers[i].iter(); other.valid(); other.advance()) {
+ for(DBIDMIter other = transfers[i].iter(); other.valid(); other.advance()) {
Meta c2 = metas.get(other);
- if (c.gain(i) + c2.gain(c.primary) > 0) {
+ if(c.gain(i) + c2.gain(c.primary) > 0) {
transfer(metas, c2, dest, source, other, c.primary);
transfer(metas, c, source, dest, id, i);
active += 2;
@@ -316,7 +316,7 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
}
}
// If cluster sizes allow, move a single object.
- if (c.gain(i) > 0 && (dest.size() < maxsize && source.size() > minsize)) {
+ if(c.gain(i) > 0 && (dest.size() < maxsize && source.size() > minsize)) {
transfer(metas, c, source, dest, id, i);
active += 1;
transferred = true;
@@ -325,7 +325,7 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
}
// If the object would prefer a different cluster, put in outgoing
// transfer list.
- if (!transferred && (c.dists[c.primary] > c.dists[c.secondary])) {
+ if(!transferred && (c.dists[c.primary] > c.dists[c.secondary])) {
transfers[c.primary].add(id);
}
}
@@ -333,14 +333,14 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
// considering more than one object?
int pending = 0;
// Clear transfer lists for next iteration.
- for (int i = 0; i < k; i++) {
+ for(int i = 0; i < k; i++) {
pending += transfers[i].size();
transfers[i].clear();
}
- if (LOG.isDebuggingFine()) {
+ if(LOG.isDebuggingFine()) {
LOG.debugFine("Performed " + active + " transfers in iteration " + iter + " skipped " + pending);
}
- if (active <= 0) {
+ if(active <= 0) {
break;
}
// Recompute means after reassignment
@@ -486,27 +486,27 @@ public class SameSizeKMeansAlgorithm<V extends NumberVector<?>> extends Abstract
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
ObjectParameter<PrimitiveDoubleDistanceFunction<? super NumberVector<?>>> distanceFunctionP = makeParameterDistanceFunction(SquaredEuclideanDistanceFunction.class, PrimitiveDoubleDistanceFunction.class);
- if (config.grab(distanceFunctionP)) {
+ if(config.grab(distanceFunctionP)) {
distanceFunction = distanceFunctionP.instantiateClass(config);
- if (!(distanceFunction instanceof EuclideanDistanceFunction) && !(distanceFunction instanceof SquaredEuclideanDistanceFunction)) {
+ if(!(distanceFunction instanceof EuclideanDistanceFunction) && !(distanceFunction instanceof SquaredEuclideanDistanceFunction)) {
LOG.warning("k-means optimizes the sum of squares - it should be used with squared euclidean distance and may stop converging otherwise!");
}
}
IntParameter kP = new IntParameter(K_ID);
- kP.addConstraint(new GreaterConstraint(1));
- if (config.grab(kP)) {
+ kP.addConstraint(CommonConstraints.GREATER_THAN_ONE_INT);
+ if(config.grab(kP)) {
k = kP.getValue();
}
ObjectParameter<KMeansInitialization<V>> initialP = new ObjectParameter<>(INIT_ID, KMeansInitialization.class, KMeansPlusPlusInitialMeans.class);
- if (config.grab(initialP)) {
+ if(config.grab(initialP)) {
initializer = initialP.instantiateClass(config);
}
IntParameter maxiterP = new IntParameter(MAXITER_ID, -1);
- maxiterP.addConstraint(new GreaterEqualConstraint(-1));
- if (config.grab(maxiterP)) {
+ maxiterP.addConstraint(CommonConstraints.GREATER_EQUAL_MINUSONE_INT);
+ if(config.grab(maxiterP)) {
maxiter = maxiterP.intValue();
}
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-21 11:26:43 +0000